Big Data Applications Research Articles

Enterprise asset management and accounting using the Internet of Things and big data

With the rapid development of information technology, the Internet of Things (IoT) and big data have gradually become the core tools of modern enterprise management and decision-making. This study explores the application and potential value of IoT and big data in the field of enterprise assets and accounting. Through strategic questionnaire design and multi-channel data collection, a corresponding data model is constructed, and the effectiveness of the model is further verified. The results show that these technologies can not only improve the management efficiency of enterprises, but also provide more accurate and timely data support for corporate decision-making. However, in practical applications, enterprises also need to face data integrity, technology updates, and a host of other challenges. This study not only provides enterprises with an opportunity to gain an in-depth understanding of these technologies, but also provides valuable implications for future research and applications.

Impact of digital economy development on labor force employment

Since the first year of the commercial application of big data, the digital economy has become an important driving force for global economic development and has had a huge impact on the labor employment market. This paper aims to explore how the development of the digital economy reshapes the employment pattern of the labor market. By analyzing the core features of the digital economy, which is highly informative, networked, and intelligent, it reveals the dual effects of the digital economy on the total and structural employment of the workforce and puts forward policy recommendations to address the employment challenges posed by the digital economy. The study finds that, on the one hand, the application of automation technology and artificial intelligence has reduced the demand for traditional low-skilled labor, triggering employment reductions in some industries; on the other hand, emerging digital technologies have created new career opportunities, especially in the fields of data analysis, software development, and digital marketing, which have brought more employment opportunities for high-skilled labor. In addition, the digital economy has given rise to numerous flexible forms of employment.

Teaching Quality Analysis of Health Management Professionals in Colleges and Universities Based on Big Data Decision Tree Algorithm

In this study, we investigate whether the big data decision tree approach can be utilised to evaluate the effectiveness of teaching in higher education by health management professors. In light of the expanding need for high-quality healthcare education by the professionals, it is absolutely necessary to evaluate and enhance the teaching efficacy of healthcare management specialists. The application of big data analytics towards the creation of a method for the evaluation of instructors that is both objective and precise is the focus of this study. Every educational system may be broken down into its two primary components: teachers and pupils. The students are not only passive recipients of the instruction that their teachers provide. All of the desired instructional achievements must be driven by the effort of the students in order to fall within the category of "subjective initiative." Therefore, the first step in any educational activity should be encouraging pupils to build their own sense of initiative and intrinsic motivation to learn. This should be the case in any educational endeavour. If teachers have access to and make use of reliable employment data for statistical analysis, they will be able to have a more significant impact on the kids they teach. Based on a preliminary set of actual university employment data from 2012, this study compares the results of employing the C4.5 approach with a decision tree generating technique for the assessment of the teaching quality by the professionals. Both approaches were used to analyse the data. The findings demonstrate that a straightforward decision tree structure may be obtained using the decision tree approach that makes use of the multiscale rough set model. In addition, our approaches don't need to use overlapping data sets, and they're quite efficient from a computing standpoint. The relevance of this study lies in the fact that it sheds light on how the expertise and motives of teachers who are healthcare professionals to be precise, reflect on the learning of their students and their relevant field skills.

Regulating the Rational Use of Antimicrobial Drugs and Improving the Quality of Healthcare Big Data: A Development and Usability Study as A Prelude to AI Assisted Healthcare

Background: Healthcare big data has become an important strategic resource, and the study of applying it to the rational application of antimicrobial drugs is of great significance in improving and optimizing the various tasks of medicine and health and promoting the development of social health. Based on its own characteristics, the issue of data quality has important research value in promoting data output and application. Objective: To optimize the status quo of antimicrobial drug use in medical institutions, to explore the value of healthcare big data, and to explore the data governance methods in the era of digital intelligence in order to build a good ecosystem for the application of healthcare big data. Methods: This paper constructs a set of intelligent full-closed-loop antimicrobial rational application management platform integrated with data quality, adopts PDCA cycle management mode, analyzes the problems in the process of applying healthcare big data and gives the corresponding solutions, authorizes according to the personnel's duties, and guarantees the data security. Results: Since the platform went online in January 2021, the antimicrobial drug use intensity indicator of a hospital has decreased year by year from 38 to 34.4 as of June 2023, effectively standardizing the use of antimicrobial drugs. The time consumed for the statistics of antimicrobial use intensity related data has decreased from an average of 30min to 2min, which has significantly improved the work efficiency.Healthcare professionals are able to monitor indicator data in real time, with or without abnormal cases, and fully grasp the trend of indicators. At the same time, the patient's medication can be viewed at any time, and the condition can be grasped in a timely manner. Conclusions: The platform not only effectively solves the problem of information barriers, but also accurately determines the data source and statistical logic of indicator collection, reflecting the actual clinical diagnosis and treatment, and at the same time greatly reduces labor costs and optimizes the workflow.The practical experience of platform construction has important reference significance for the governance and application of healthcare big data, which provides data support and technical guarantee for the scientific, standardized and refined management of hospitals.

Opportunities and Challenges in Health Insurance Statistics in the Era of Big Data

With the rapid advancement of big data technology, health insurance statistics are undergoing unprecedented transformations. This study explores the opportunities and challenges of health insurance statistics in the context of big data, offering theoretical frameworks and strategic recommendations. Through a comprehensive literature review and theoretical analysis, the study systematically examines related research on the application of big data in health insurance statistics globally. It analyzes how big data technology enhances the efficiency of data collection, processing, and analysis in health insurance, while also addressing issues like data security, privacy protection, and talent shortages. The research involves an in-depth analysis of big data technology characteristics and a reevaluation of health insurance statistical workflows. The findings indicate that big data offers opportunities for improved data processing capabilities and enhanced decision support in health insurance statistics, while simultaneously posing challenges in data governance, privacy protection, and talent development. To fully leverage the advantages of big data, an improved data management system, strengthened data security and privacy measures, and the cultivation of professionals skilled in big data analysis are necessary. This study provides theoretical support and practical guidance for the development of health insurance statistics in the big data context.

Application of Big Data in Electrical Engineering

Chinese With the continuous progress of science and technology and the rapid development of information technology, big data has become a hot topic in today's society. The application of big data has penetrated into various fields, among which electronic engineering is one of the important application fields. This thesis focuses on the application of big data in electronic engineering and analyses its specific applications in data acquisition and processing, model building and optimization, fault diagnosis and prediction, and intelligent decision-making and control. The study elaborates on the application of big data in electronic engineering. In terms of data acquisition and processing, the applications of sensor data acquisition and processing and signal processing and analysis are highlighted. In the area of model building and optimisation, the paper explores methods for model building and optimisation of electronic devices based on big data. In the area of fault diagnosis and prediction, the paper proposes a fault diagnosis and prediction method based on big data. Finally, in the area of intelligent decision-making and control, the paper discusses intelligent decision-making and control methods based on big data.

中国面部识别技术发展考察

Recently, the rapid development of facial recognition technology, which is an important field of artificial intelligence, has attracted more and more attention worldwide due to the integration of big data and artificial intelligence technology. This technology has been widely applied in various fields and continues to develop with many advantages and convenience. However, the concerns and potential risks associated with the widespread use of facial recognition technology are also significant. Therefore, this study aims to comprehensively and critically review the development and risks of this technology, focusing on the current situation in China. It is intended to offer guidance and support for the ethical advancement of contemporary facial recognition technology as well as to promote responsible oversight and regulation. In this paper, first, the development process of facial recognition technology such as the technological origin and core technological innovation in the world and China is reviewed. Next, it also examines the application and convergence of big data and artificial intelligence algorithms in facial recognition technology. Furthermore, it analyzes the latest development of facial recognition technology in China, including the driving role of the government and companies and trends in major application fields. Simultaneously, this discourse delves into the extensive risks and challenges associated with facial recognition technology, such as privacy, data security, potential misuse, ethics, and legal regulations. To put it another way, the practical risks of facial recognition technology are focused on its specific applications in the fields of public safety, commerce, and social management, highlighting the resulting technical vulnerabilities, privacy threats, and abuse of social monitoring. Finally, this study summarizes the development status of facial recognition technology, points out the major problems and risks facing it once again, and presents appropriate countermeasures and policy recommendations.

Big Data and Machine Learning in Defence

In the era of data-driven warfare, the integration of big data and machine learning (ML) techniques has become paramount for enhancing defence capabilities. This research report delves into the applications of big data and ML in the defence sector, exploring their potential to revolutionize intelligence gathering, strategic decision-making, and operational efficiency. By leveraging vast amounts of data and advanced algorithms, these technologies offer unprecedented opportunities for threat detection, predictive analysis, and optimized resource allocation. However, their adoption also raises critical concerns regarding data privacy, ethical implications, and the potential for misuse. This report aims to provide a comprehensive understanding of the current state of big data and ML in defence, while examining the challenges and ethical considerations that must be addressed to ensure responsible and effective implementation.

Towards human-AI collaborative urban science research enabled by pre-trained large language models

Pre-trained large language models (PLMs) have the potential to support urban science research through content creation, information extraction, assisted programming, text classification, and other technical advances. In this research, we explored the opportunities, challenges, and prospects of PLMs in urban science research. Specifically, we discussed potential applications of PLMs to urban institution, urban space, urban information, and citizen behaviors research through seven examples using ChatGPT. We also examined the challenges of PLMs in urban science research from both technical and social perspectives. The prospects of the application of PLMs in urban science research were then proposed. We found that PLMs can effectively aid in understanding complex concepts in urban science, facilitate urban spatial form identification, assist in disaster monitoring, sense public sentiment and so on. They have expanded the breadth of urban research in terms of content, increased the depth and efficiency of the application of multi-source big data in urban research, and enhanced the interaction between urban research and other disciplines. At the same time, however, the applications of PLMs in urban science research face evident threats, such as technical limitations, security, privacy, and social bias. The development of fundamental models based on domain knowledge and human-AI collaboration may help improve PLMs to support urban science research in future.

Unravelling the potential of Big Data-driven decision-making in sustainable water irrigation: An AI perspective

Big Data-driven initiatives have transformed several industries by using large databases to extract vital information and enhance operations. In the field of sustainable water irrigation, Big Data enables intelligent water management, allowing for exact resource allocation, monitoring and analysis. This combination enables smart decision-making, increasing productivity in agriculture while reducing water consumption and environmental impacts. The revolutionary influence of data-driven approaches on managing water resources for sustainable and efficient irrigation practices has significant potential. The research highlights the importance of Big Data-driven approaches in water management by examining their applicability. It explores the artificial intelligence (AI) techniques for water irrigation that is sustainable and determines significant affecting factors efficient water management. The study encourages AI in intelligent water management systems and explains the application of big data in irrigation. It illustrates how smart water management which includes hydrated irrigation and predictive water infrastructure can contribute to AI-driven water management. To handle complicated water supply problems, the research presents an estimated water level model, highlighting the integration of AI and indicating the revolutionary potential of these techniques in attaining sustainable water irrigation. We highlight the novel possibilities of big data-driven methods for sustainable water irrigation, illustrating the essential part of AI techniques in effective water resource allocation.

Cybercrimes in the Darknet and Their Detections: A Comprehensive Analysis and Future Directions

Although the Dark web was originally used for maintaining privacy-sensitive communication for business or intelligence services for defence, government and business organizations, fighting against censorship and blocked content, later, the advantage of technologies behind the Dark web were abused by criminals to conduct crimes which involve drug dealing to the contract of assassinations in a widespread manner. Since the communication remains secure and untraceable, criminals can easily use dark web service via The Onion Router (TOR), can hide their illegal motives and can conceal their criminal activities. This makes it very difficult to monitor and detect cybercrimes over the dark web. With the evolution of machine learning, natural language processing techniques, computational big data applications and hardware, there is a growing interest in exploiting dark web data to monitor and detect criminal activities. Due to the anonymity provided by the Dark Web, the rapid disappearance and the change of the uniform resource locators (URLs) of the resources, it is not as easy to crawl the Drak web and get the data as the usual surface web which limits the researchers and law enforcement agencies to analyse the data. Therefore, there is an urgent need to study the technology behind the Dark web, its widespread abuse, its impact on society and the existing systems, to identify the sources of drug deal or terrorism activities. In this research, we analysed the predominant darker sides of the world wide web (WWW), their volumes, their contents and their ratios. We have performed the analysis of the larger malicious or hidden activities that occupy the major portions of the Dark net; tools and techniques used to identify cybercrimes which happen inside the dark web. We applied a systematic literature review (SLR) approach on the resources where the actual dark net data have been used for research purposes in several areas. From this SLR, we identified the approaches (tools and algorithms) which have been applied to analyse the Dark net data, the key gaps as well as the key contributions of the existing works in the literature. In our study, we find the main challenges to crawl the dark web and collect forum data are: scalability of crawler, content selection trade off, and social obligation for TOR crawler and the limitations of techniques used in automatic sentiment analysis to understand criminals’ forums and thereby monitor the forums. From the comprehensive analysis of existing tools, our study summarizes the most tools. However the forum topics rapidly change as their sources changes; criminals inject noises to obfuscate the forum’s main topic and thus remain undetectable. Therefore supervised techniques fail to address the above challenges. Semi-supervised techniques would be an interesting research direction.

Adoption and use factors of artificial intelligence and big data by citizens

The impact of artificial intelligence on people’s lives is demonstrated today. Previous literature has shown that the use of a specific technology is directly linked to the individuals’ intention to use it. The aim of this paper is to study the factors that determine the adoption and use of artificial intelligence and big data in Spain, using a research model based on the Unified Theory of Acceptance and Use of Technology (UTAUT), proposed by Venkatesh et al. (2003). This work addresses the specific gap in the validation of the original theoretical model of UTAUT in two dimensions, with respect to the adoption of artificial intelligence by citizens and with respect to the factors that influence this adoption, evaluating the previous ones and proposing some new ones considering the current context. The methodology used is based on a national survey, and it analyzes the research model using the statistical technique of Partial Least Squares Structural Equation Modelling (PLS-SEM), which details the mediating and moderating relationships between constructs. The results show that Intention to Use has a direct positive influence on the Use of artificial Intelligence and big data, confirming previous literature. Performance Expectancy is the strongest predictor of Intention to Use, and indirectly of the adoption of artificial intelligence and big data applications. Effort Expectancy, in its application to the adoption of AI and big data by citizens, is an indirect determinant mediated by the Intention to Use, but its total effect (direct + indirect) is not significant.

Sustainable Landscape Design and Traditional Villages in Xuzhou, Jiangsu: Low-cost Strategies and Big Data Applications Influencing AI Integration

Big Data usage and Artificial Intelligence (AI) technology combined offer a potential approach to solving challenging problems. AI-driven solutions provide insightful analysis and creative solutions by utilizing the power of big data analytics. With an emphasis on the mediating role of technological literacy and the moderating effect of resource availability, this study investigates the effects of low-cost techniques, the usage of Big Data, and the integration of Artificial Intelligence (AI) on sustainability in landscape design. The purpose of this study is to look at the intricate connections between these factors and how they affect sustainable landscape design methods and results as a whole. A standardized questionnaire was answered by a sample of 458 landscape experts as part of a quantitative approach. Smart PLS (Partial Least Squares), which incorporates evaluations of measurement models, structural models, and mediation and moderation studies, was utilized for data analysis. The study found that using Big Data, implementing low-cost techniques, and incorporating AI all had very favourable effects on sustainability in landscape design. The efficient use of Big Data and AI was found to be mediated by technological literacy, highlighting the importance of this concept in this context. Additionally, resource availability emerged as a critical moderating factor, influencing the strength of these relationships. This research contributes to the field by offering a holistic understanding of the dynamics within sustainable landscape design, emphasizing the importance of integration of AI and utilization of Big Data. It provides practical insights for landscape professionals, informs policy development, and advances educational curricula about AI and Big Data in landscape architecture. The study's limitations include potential response bias due to self-reported data and the cross-sectional design, which restricts the establishment of causal relationships. Additionally, the study focused on professionals, limiting the generalizability of findings to broader community perspectives.

Do topic and sentiment matter? Predictive power of online reviews for hotel demand forecasting

Studies integrating textual data for forecasting have mainly focused on the overall sentiment reflected in text. Yet textual data convey various types of information, such as review topics, that can be beneficial when forecasting hotel demand. This study aims to combine topic modeling and sentiment analysis to improve forecasting performance of hotel demand. Specifically, the latent Dirichlet allocation (LDA) topic modeling technique and the long short-term memory (LSTM) model are employed to construct topic-based sentiment indices. The autoregressive integrated moving average (ARIMA) with explanatory variable–type models and mixed data sampling (MIDAS) models are adopted for the evaluation of predictive power. Results reveal that MIDAS forecasting with topic–sentiment and COVID-19 variables generates most accurate forecasts. The findings contextualize the application of online textual big data in hotel demand forecasting research. Hotel management can utilize these online data for short-term forecasting to facilitate crowd management and respond more effectively to unforeseen public health events.

The Application of Big Data and Privacy Impacts Research in Information Technology

The Application of Big Data and Privacy Impacts Research in Information Technology

Self-efficacy of Data Literacy among Primary and Secondary School Teachers in Guangdong Province

The application of educational big data provides new basis for teachers to improve teaching and promote student development. However, there are still many challenges in using data to solve complex educational problems. Teacher data literacy has become the key to transforming educational data into effective teaching strategies. The study sought to gain a better understanding of the current state of primary and secondary school Teachers self-efficacy of data literacy and put forward some suggestions for teacher data literacy training. This study used a quantitative methodology. 813 primary and secondary school teachers from Guangdong Province, China, completed a questionnaire on self-efficacy of data literacy. (1) Primary and secondary school teachers reported significantly high levels of self-efficacy in data literacy (mean=3.624). Among the dimensions, the data awareness and ethics dimension had the highest self-efficacy (mean=3.738) and the data research dimension had a lower self-efficacy (mean=3.358). (2)There were significant differences in the primary and secondary school teachers’ self-efficacy of data literacy with different ages, educational backgrounds, and school locations. Primary and secondary school teachers generally recognized the importance of data literacy training. Older teachers who worked in rural schools and had low levels of education have the lowest level of self-efficacy for data literacy. For primary and secondary school teachers' low level of data analysis skills and weak data transformation strategy ability, teacher data literacy training adopts an embedded teamwork approach, integrating multiple resources to build a data team, integrating big data technology throughout the entire teaching process, and helping teachers to use new data and technologies to understand students' needs in a timely manner, to select teaching methods, and to solve real-world problems. It makes data literacy the foundation of teachers' teaching and promotes the development of all students.

Research on The Times Value of Village Public Sports Space Planning: Theoretical Logic, Practical Approach and Big Data Application Exploration in The Background of Rural Revitalization

It is the key to the implementation of the rural revitalization strategy and the inevitable way to use big data to help the spatial planning of village sports, improve the efficiency of spatial allocation of resources, and promote the high-quality development of rural sports. This paper mainly adopts research methods such as literature materials and logical analysis to explore the Times value of village public sports space planning, expound the theoretical logic and practical difficulties of village public sports space planning, and then propose practical approaches and big data application exploration. The research finds that the Times value of village public sports space planning is reflected in the aspects of increasing the effective supply of public sports space, stimulating the spillover value of public sports space, and satisfying the ardent yearning for a better life. Based on the theoretical logic of continuous co-construction, co-governance and sharing, dynamic improvement of suitable adaptation, long-term improvement of convenience and safety, and steady integration into livability and business. It is found that the planning of village public sports space is faced with such realistic difficulties as the imbalance of resource supply standards and allocation among villages, the weakening of residents' participation in planning, the lack of guidance in technical operation and planning layout, and the insufficient coupling degree between public sports space and village environment. It is proposed that the planning of village public sports space should follow the practice path of overall planning of inter-village sports service resource supply, mobilizing residents' enthusiasm to participate in spatial planning, enabling the layout and location of sports space with digital technology, and forming diversified public sports service supply.

Application of big data and artificial intelligence inmental health prediction and intervention

In this article, we delve into applying Convolutional Neural Networks (CNNs) and big data in predicting and intervening in mental health issues, emphasizing the potential for early detection and personalized treatment. By analyzing patterns in social media data, CNNs can identify indicators of mental health conditions, offering insights for tailored interventions. The discussion highlights the importance of addressing privacy, data security, and algorithmic bias to ensure ethical implementation. Future directions include enhancing predictive accuracy, expanding AI applications in therapy, fostering interdisciplinary collaborations, developing ethical frameworks, and engaging the public. Embracing these technologies in mental health care promises significant advancements but necessitates careful consideration of ethical imperatives to maximize benefits while safeguarding patient welfare.

Big data application and corporate investment decisions: Evidence from A-share listed companies in China

Based on a natural language processing (NLP) method and annual reports from A-share listed companies in China from 2011 to 2020, this paper constructs a big data index for the corporate level and studies the impact of big data on corporate investment decisions. The study finds that big data significantly improves corporate investment decisions. From the perspective of index decomposition, data mining and mobile service are found to significantly improve corporate investment efficiency, while algorithm development and intelligent manufacturing mainly enhance the corporate investment level. The study also notes that heterogeneity may exist due to differences in financial, innovation, and legal environments. In terms of mechanisms, big data can positively influence corporate investment decisions by improving R&D investment and reducing transaction costs.

Big Data in organizations: Exploring the adoption of Big Data applications and their impact on organizations in China and the Netherlands

Digital technology has rapidly been transforming how organizations operate. However, the literature in management studies has only just started to problematize the fundamental inter-relation of digital technology and organizing and we lack sound data about the actual breadth and depth of these changes. This study therefore explores the state of the implementation of Big Data applications in a wide range of organizations in China and the Netherlands and the impact on organizational structures and processes. Our findings show that most organizations are still in an experimental phase at best. We can therefore observe an evolutionary model of technology adoption